This page contains exploratory analysis for the trawl data from JSOES.

Data Overview

We will first explore the most common and most abundant taxa in this dataset.

Top ten taxa by mean density.
species mean_n_per_km sd_n_per_km
sea_nettle 44.4 264.8
water_jelly 41.4 239.7
california_market_squid 14.4 88.6
coho_salmon_yearling 2.3 5.2
sablefish 1.9 24.4
chum_salmon_juvenile 1.2 4.9
chinook_salmon_subyearling_interior_fa 0.6 2.3
moon_jelly 0.4 9.0
chinook_salmon_subyearling_scg_f 0.3 3.1
chinook_salmon_yearling_interior_fa 0.3 0.7
Top ten taxa by frequency of occurrence.
species n prop_samples
water_jelly 749 0.56
coho_salmon_yearling 739 0.55
california_market_squid 509 0.38
sea_nettle 509 0.38
chinook_salmon_yearling_interior_fa 445 0.33
chum_salmon_juvenile 415 0.31
chinook_salmon_mixed_age_juvenile 337 0.25
chinook_salmon_yearling_interior_sp 334 0.25
chinook_salmon_subyearling_interior_fa 323 0.24
wolf_eel 252 0.19

Annual time series

This Shiny app can be used to explore the abundances of different taxa across the full length of the time series. In this Shiny app, I take the mean log density across the survey region to create a simple index of abundance.

What salmon are we getting?

By frequency of occurrence: - Coho salmon yearling (coho_salmon_yearling) - 55% - My understanding is that if these are Columbia River Coho, they’re most likely from downstream of BON and therefore aren’t good candidates for SAR estimates - Chinook yearling Interior Fall (chinook_salmon_yearling_interior_fa) - 33% - Should double check where these are coming from - Chum salmon juvenile (chum_salmon_juvenile) - 30% - I don’t think people really care about Chum. Sorry Chum. - Chinook yearling Interior Spring (chinook_salmon_yearling_interior_sp) - 25% - definitely include - Chinook salmon mixed age juvenile (chinook_salmon_mixed_age_juvenile) - 25% - unknown origin? - Chinook subyearling Interior Fall (chinook_salmon_subyearling_interior_fa) - 24% - definitely include - Chinook salmon yearling West Cascade spring (lower Columbia) (chinook_salmon_yearling_wc_sp) - 13% - Lower Columbia - likely downstream of BON - Chinook salmon yearling Willamette River Spring (chinook_salmon_yearling_wr_sp) - 13% - Willamette River ESU, downstream of BON - Sockeye salmon juvenile (sockeye_salmon_juvenile) - 12% - unknown origin - Chinook Spring Creek Group fall (chinook_salmon_subyearling_scg_f) - 8% - Lower Columbia - likely downstream of BON - Chinook salmon Willamette River Subyearling Spring (chinook_salmon_subyearling_wr_sp) - 8% - Willamette River ESU, downstream of BON

We need to understand what GSI groups are even worth modeling SAR data for

## # A tibble: 40 × 3
##    species                                    n prop_samples
##    <chr>                                  <int>        <dbl>
##  1 coho_salmon_yearling                     739       0.553 
##  2 chinook_salmon_yearling_interior_fa      445       0.333 
##  3 chum_salmon_juvenile                     415       0.310 
##  4 chinook_salmon_mixed_age_juvenile        337       0.252 
##  5 chinook_salmon_yearling_interior_sp      334       0.250 
##  6 chinook_salmon_subyearling_interior_fa   323       0.242 
##  7 chinook_salmon_yearling_wc_sp            176       0.132 
##  8 chinook_salmon_yearling_wr_sp            171       0.128 
##  9 sockeye_salmon_juvenile                  166       0.124 
## 10 chinook_salmon_subyearling_scg_f         116       0.0868
## # ℹ 30 more rows
## # A tibble: 11 × 3
##    species                                    n prop_samples
##    <chr>                                  <int>        <dbl>
##  1 coho_salmon_yearling                     739       0.553 
##  2 chinook_salmon_yearling_interior_fa      445       0.333 
##  3 chum_salmon_juvenile                     415       0.310 
##  4 chinook_salmon_mixed_age_juvenile        337       0.252 
##  5 chinook_salmon_yearling_interior_sp      334       0.250 
##  6 chinook_salmon_subyearling_interior_fa   323       0.242 
##  7 chinook_salmon_yearling_wc_sp            176       0.132 
##  8 chinook_salmon_yearling_wr_sp            171       0.128 
##  9 sockeye_salmon_juvenile                  166       0.124 
## 10 chinook_salmon_subyearling_scg_f         116       0.0868
## 11 chinook_salmon_subyearling_wr_sp         103       0.0770

Plotting distributions of focal Chinook stocks

Our study focuses on Interior Chinook. Here, I visualize the distributions of Interior Fall Chinook (yearlings and subyearlings) and Interior Spring Chinook (yearlings)

Exploration of broad groups

In addition to our focal Chinook stocks, the JSOES trawl also captures some other key taxa. These include: - Market Squid (“california_market_squid”) - prey item - Sablefish (“sablefish”) - competitor - American shad (“american_shad”) - prey item - Pacific Pompano (“pacific_pompano”) - competitor

The distributions of these taxa are shown below:

Temporal and Spatial Autocorrelation

Before fitting any spatiotemporal models, we must explore the spatial and temporal autocorrelation in the data.


Temporal structure

We can first inspect the autocorrelation in our four focal taxa, at the scale of the full survey region:

None of the four taxa investigated here show temporal autocorrelation.

I also investigated temporal autocorrelation at the scale of individual stations, and found little evidence for temporal autocorrelation at this scale for these four taxa.


Spatial structure

To investigate spatial autocorrelation, I calculated a metric known as Moran’s I. Moran’s I is a measure of the overall clustering of the spatial data and tests if there is support to reject the null hypothesis of no spatial structure. Given that we did not see much evidence for temporal autocorrelation, I calculated Moran’s I separately for each year. To summarize the Moran’s I results, I show the p-value for Moran’s I for each year, with the blue dashed line showing a p-value of 0.05.









Based on the Moran’s I results, we see that there is evidence for spatial clustering in some years but not others. There are also notably more years where the Moran’s I value is significant for these taxa than there are for the two copepod species examined in the JSOES Bongo exploratory analysis.